Dialogue Enhancement and Listening Effort in Broadcast Audio: A Multimodal Evaluation

TL;DR

This study investigates how dialogue enhancement in broadcast audio affects listening effort by measuring pupil size and self-reported effort, demonstrating that improved audio clarity reduces listening effort and pupil dilation.

Contribution

It provides a multimodal evaluation of dialogue enhancement effects on listening effort using pupil size and self-report, including the impact of source separation techniques.

Findings

Dialogue enhancement reduces pupil size and listening effort.

Source separation further improves listening experience.

Multimodal measures correlate with subjective effort reports.

Abstract

Dialogue enhancement (DE) plays a vital role in broadcasting, enabling the personalization of the relative level between foreground speech and background music and effects. DE has been shown to improve the quality of experience, intelligibility, and self-reported listening effort (LE). A physiological indicator of LE known from audiology studies is pupil size. The relation between pupil size and LE is typically studied using artificial sentences and background noises not encountered in broadcast content. This work evaluates the effect of DE on LE in a multimodal manner that includes pupil size (tracked by a VR headset) and real-world audio excerpts from TV. Under ideal listening conditions, 28 normal-hearing participants listened to 30 audio excerpts presented in random order and processed by conditions varying the relative level between foreground and background audio. One of these conditions employed a recently proposed source separation system to attenuate the background given the original mixture as the sole input. After listening to each excerpt, subjects were asked to repeat the heard sentence and self-report the LE. Mean pupil dilation and peak pupil dilation were analyzed and compared with the self-report and the word recall rate. The multimodal evaluation shows a consistent trend of decreasing LE along with decreasing background level. DE, also when enabled by source separation, significantly reduces the pupil size as well as the self-reported LE. This highlights the benefit of personalization functionalities at the user's end.